Automatic particle picking and multi-class classification in cryo-electron tomograms

Chen X, Chen Y, Schuller JM, Navab N, Förster F (2014)

Publication Type: Conference contribution

Publication year: 2014

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 838-841

Conference Proceedings Title: 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Event location: Beijing, CHN

ISBN: 9781467319591

DOI: 10.1109/isbi.2014.6868001

Abstract

Macromolecular structure determination using cryo-electron tomography requires large amount of subtomograms depicting the same molecule, which are averaged. In this paper, we propose a novel automatic particle picking and classification method for cryo-electron tomograms. The workflow comprises two stages: detection and classification. The detection method consists of a template-free picking procedure based on anisotropic diffusion filtering and connected component analysis. For classification, a novel 3D rotation invariant feature descriptor named Sphere Ring Haar and a hierarchical classification algorithm consisting of two machine learning models (DBSCAN and random forest) are proposed. The performance of our method is superior compared to template matching based methods and we achieved over 90% true positive rates for detection of proteasomes and ribosomes in experimental data.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Max-Planck-Institut für Biochemie / Max Planck Institute of Biochemistry DE Germany (DE)

How to cite

APA:

Chen, X., Chen, Y., Schuller, J.M., Navab, N., & Förster, F. (2014). Automatic particle picking and multi-class classification in cryo-electron tomograms. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 838-841). Beijing, CHN: Institute of Electrical and Electronics Engineers Inc..

MLA:

Chen, Xuanli, et al. "Automatic particle picking and multi-class classification in cryo-electron tomograms." Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, CHN Institute of Electrical and Electronics Engineers Inc., 2014. 838-841.

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